The applicability of the present data augmentation techniques to machine learning-based temporal prediction is further investigated considering a cylinder wake and the NOAA sea surface temperature data.
Neural network-based surrogate modeling for a numerical simulation is one of the promising uses in nonlinear dynamical systems.
Thanks to its rapid estimation, a neural network-based method can estimate the future state of the dynamics with significantly shorter computational time compared to the traditional numerical approach.
For instance, Fukami et al. [1]} proposed an inflow driver for numerical simulation of turbulence using neural networks.
Another approaches consisted with long short-term memory [2]}, [3]}, [4]}, [5]} and sparse identification of nonlinear dynamics [6]}, [7]} are also promising techniques in estimating the temporal evolution of the flows.
